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Deep Learning for Cancer Diagnosis

Edited by Utku Kose, Jafar Alzubi 

to be published by Springer

Objective of this edited book is to provide recent advanced applications of Deep Learning for diagnosing cancer. As it is known, Artificial Intelligence has taken many steps away for effective solutions in the field of medical. Changing from information discovery to diagnosis works, or automated healthcare support to optimized physical devices; it is possible to see many different applications of Artificial Intelligence for a better, healthy world. In this context, Deep Learning is a recent and remarkable sub-field, which can deal with huge data for more accurate results. Except from many other fields of the modern life, the field of medical is also interested too much in applying Deep Learning solutions to the problems. As a vital research topic, medical diagnosis is among research efforts in which Deep Learning oriented solutions are often employed.

The main coverage of the Deep Learning applications included in the book will be cancer diagnosis. Because cancer is the most dangerous illness of today’s world and there is a great effort to deal with different types of cancers. It is critical that MIT developed an Artificial Intelligence system, which can detect breast cancer up to five years before it appears in a real manner. Except from the effective use of an Artificial Intelligence solution, the early detection-diagnosis of the cancer is very important for the recent advances in the literature. So, the book proposed here aims to inform the audience about the most advanced, recent applications of Deep Learning systems for understanding the state of the developments, innovations and give ideas to practitioners, students, and researchers to perform further effective works.

The book will cover only advanced, quality applications aiming to diagnose different types of cancers such as breast cancer, lung cancer, skin cancer, brain cancer-tumours, or prostate cancer…etc. Because diagnose of cancer generally include apply of different medical data analyses and i.e. image processing approaches, the book will enable readers to have a deeper understanding of the relevant solutions of medical diagnosis in the light of the strong collection of techniques (i.e. CNN, LSTM, Autoencoder Networks) of the Deep Learning. It is also remarkable to have both positive and negative findings, and also especially early detection-diagnosis oriented recent works in this manner.

Submission Guidelines

All submissions should be done by email to: utkukose@gmail.com

All papers must be original and not simultaneously submitted to another book project, journal or conference. The following important dates will be considered for the submissions:

• Full Paper Submission: 08.03.2020 
• (you may ask to utkukose@gmail.com for pre-proposal and full chapter preparation documents)

List of Topics (as not limited to)

• Deep Learning for Enhancing Cancer Diagnosis,
• Deep Learning for Diagnosing Rare Cancer Types,
• Deep Learning for Histopathological Diagnosis,
• Deep Learning for Cancer Diagnosis Over Hybrid Data,
• Deep Learning Supported Approaches for Cancer Diagnosis,
• Improved Deep Learning Techniques for Better Cancer Diagnosis,
• Effective Use of Deep Learning and Image Processing for Cancer Diagnosis,
• Negative Results from Research on Deep Learning for Cancer Diagnosis
• ...etc.

Editors

• Utku Kose, PhD. (Suleyman Demirel University, Turkey)
• Jafar Alzubi, PhD. (Al-Balqa Applied Univ., Jordan)

Publication

'Deep Learning for Cancer Diagnosis' will be published by Springer.

Contact

All questions about submissions should be emailed to: utkukose@gmail.com